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Graph-Based Feature Learning for Cross-Project Software Defect Prediction

computers, Materials & Continua 2023年第10期77卷 161-180页

作者： Ahmed Abdu Zhengjun Zhai Hakim A.Abdo Redhwan Algabri Sungon Lee School of Software Northwestern Polytechnical UniversityXi’anChina School of Computer Science Northwestern Polytechnical UniversityXi’anChina Department of Computer Science Hodeidah UniversityPO Box 3114Al-HudaydahYemen Research Institute of Engineering and Technology Hanyang UniversityAnsanKorea Department of Robotics Hanyang UniversityAnsanKorea

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

关键词： Cross-project defect prediction graphs features deep learning graph embedding

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Remote Sensing Scene Classification Based on Hybrid Convolutional Neural Network and Attention Module

Remote Sensing Scene Classification Based on Hybrid Convolut...

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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Kardawi, Muhammad Yusuf Gunawan, Alexander Agung Santoso Bina Nusantara University Profesional Engineer Program Faculty of Engineering Jakarta11480 Indonesia Faculty of Computer Science University of Indonesia Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification. © 2025 IEEE.

关键词： Convolutional neural networks

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TFENet: Topological Feature Extraction Network for 6D Object Pose Estimation

TFENet: Topological Feature Extraction Network for 6D Object...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Zhihao Li, Zhengrong Lin, Jiacheng Li, Zhiyong Chen, Wenrui College of Computer Science and Electronic Engineering Hunan University Changsha China School of Robotics Hunan University Changsha China

ISBN: (纸本)9798350303759

Grasping is an essential and challenging skill for robots to interact with their environment. Accurately obtaining the 6D pose information of the grasped object is the key to successful grasping. Due to the influence of illumination changes and object textures, the previous methods had problems with low utilization of depth information and inaccurate feature information. We study feature information extraction and fusion to address these issues and proposed a topological feature extraction network (TFENet). Firstly, we design TFE module to achieve global feature extraction from object point clouds, through which features can be identified to use depth information efficiently. Secondly, we design a MFF module to fuse color and geometric embedding better. Finally, experiments show that the proposed method outperforms existing methods with two datasets, LineMOD and YCB-Video. © 2023 IEEE.

关键词： Feature extraction

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SCASAN: Spatial Central Attention-Spectral Attention Network Based Feature Extractor for Hyperspectral Image Classification

SCASAN: Spatial Central Attention-Spectral Attention Network...

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2023 China Automation Congress, CAC 2023

作者： Zhao, Wei Zhang, Hui Cao, Yihong Liu, Lizhu Su, Yingjian School of Robotics Hunan University Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9798350303759

Due to the influence of space, time, environmental changes, and types of ground objects on the lighting conditions of high-light falling images, Hyperspectral image (HSI) classification is challenging. Recently, attention-based HSI classification has received increasing attention. It uses both spatial attention and spectral attention to capture the required spatial-spectral features, thereby achieving better classification accuracy. However, it is challenging to connect the two attention mechanisms without compromising the spatial-spectral features. In addition, how to obtain the attention weight matrix which determines the importance of each channel or spatial location is also an important problem in the use of attention mechanisms. In response to the above issues, the Spatial Central Attention-Spectral Attention network (SCASAN) is designed to efficiently model the spatial-spectral information for HSI classification. In this network, we propose the novel spectral weight spatial attention (SWSA) module to solve the connection problem between spatial attention and spectral attention. Furthermore, we utilize a spatial central attention network (spatial CAN) to obtain a spatial attention weight matrix to avoid the impact of non-homogeneous adjacent pixels on the classified pixel. Our proposed approach demonstrates superior performance in hyperspectral image classification compared to state-of-the-art methods, as evidenced by comprehensive experiments conducted on three publicly available datasets. © 2023 IEEE.

关键词： Classification (of information)

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Design of an Automatic Price Tag System for Web-Based Retail Business

Design of an Automatic Price Tag System for Web-Based Retail...

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International Conference on Information Management and Technology (ICIMTech)

作者： Suryadiputra Liawatimena Devina Gunawan Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Deparment BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

Modern retail businesses face a significant challenge with the inefficiency of manually changing price labels on shelves. This manual process not only consumes valuable time and resources but also increases the likelihood of errors, leading to potential inaccuracies in pricing and a less streamlined shopping experience for customers. The proposed solution is the implementation of an Electronic Shelf Label (ESL) system that automatically displays the prices of goods on retail business shelves. This system connects a website (front end and back end) to e-paper via a Wi-Fi network and microcontroller, allowing retail business owners to update prices more easily. Additionally, buyers can search for desired items through the website. The results are the time to send data from the website to the e-paper, namely, to know the performance of the e-paper used. The average time required from 10 attempts to send data from the website to the e-paper is 20.935 seconds. Since the data is connected to the server and will be updated automatically, using this system will be more efficient than manually changing the price label, although it takes time to transfer data from the website to the e-paper.

关键词： Electric potential Microcontrollers Pricing Manuals Information management Servers Wireless fidelity Faces Business

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A study on the noise reduction method in turbid water environments using Spatial frequency characteristics 9

A study on the noise reduction method in turbid water enviro...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Koura, Ayumu Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Iizuka city Japan Hankyong National University School of ICT Robotics and Mechanical Engineering Iitc Anseong-si Korea Republic of

ISBN: (纸本)9798350363043

Scattering noise reduction is a challenging project to remove noise under scattering media conditions such as fog or turbid water. In previous study, blurring caused by scattering medium particles in fog or turbid water was removed by low-pass filtering. However, in this method, the accurate color information of the object may be lost due to rough low-frequency filtering. In addition, it is difficult to the obtain object shape information in heavy turbid environments. To solve this problem, we propose a new de-scattering algorithm that combines Fourier-domain filtering and integral imaging by creating a filter tailored to the frequency to be removed. To prove the proposed method, we perform a numerical analysis of experimental results using conventional and proposed methods. © 2024 IEEE.

关键词： Noise abatement

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A study on Image Processing Method for Noise Reduction in Digital Holographic Microscopy 9

A study on Image Processing Method for Noise Reduction in Di...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Nagashima, Masaharu Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Iizuka city Japan Iitc Hankyong National University School of Ict Robotics and Mechanical Engineering Anseong-si Korea Republic of

ISBN: (纸本)9798350363043

Digital Holographic Microscopy (DHM) can obtain three-dimensional (3D) information about the fine structure of an object by utilizing the phase information of coherent light. In DHM, during the process of reconstructing the shape of an object, phase information is extracted by windowing the sideband containing the phase information. The 3D information of the object is then obtained from the acquired phase information. When windowing the sideband in a narrow range, it can remove noise caused by the DC spectrum, but detailed shape information of the object is lost. On the other hand, when windowing in a wide range, detailed shape information of the object can be obtained, but noise from the DC spectrum increases. To solve this trade-off relationship, we propose a method of determining the range for windowing the sideband using a function and then averaging the windowed sidebands. This proposed method can reduce the impact of noise from the DC spectrum and enables the acquisition of the detailed shape information. In this paper, we explain the reconstruction algorithm of this method and compare it with conventional methods and the previous study, phase differences averaging (PDA). © 2024 IEEE.

关键词： Noise abatement

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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V-TX: A VR-Based Deployment and Evaluation System for Wearable Telexistence Robotic Systems 23

V-TX: A VR-Based Deployment and Evaluation System for Wearab...

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23rd International Conference on Cyberworlds, CW 2024

作者： Iskandar, Abdullah Aburajouh, Hala Khazer Shebli Al-Shaer, Eman Hussain Al-Yafei, Fatima Thabit Al-Arqaban, Bushra Ahmad Al-Yousef, Moneera Yang, Pinchu Halabi, Osama Al-Sada, Mohammed Nakajima, Tatsuo Waseda University Computer Science and Engineering Japan Telkom University School of Computing Indonesia Qatar University Computer Science and Engineering Qatar Waseda University Future Robotics Organization Japan Qatar University KINDI Computing Research Center Qatar

ISBN: (纸本)9798331527174

Wearable telexistence robotic systems have risen in prominence after the COVID-19 pandemic, being vital for various applications. However, their design, fabrication, and evaluation require substantial resources and are unsupported by robot simulation systems. Therefore, we present V-TX, which is a VR-based system for deploying and evaluating wearable telexistence systems. V-TX extends key telexistence systems' components with versatility for customizing different attributes based on the intended application. To demonstrate the potential of V-TX, we replicated a real telexistence robot in V-TX, called Piton, and showed a novel potential use for exploring and evaluating different robot designs, interaction methods, and deployments. We conducted a comparative evaluation between the real Piton robot and its V-TX implementation. The results show that V-TX gave results that match those of the real counterpart across the main factors of telexistence, indicating its viability as an alternative to traditional evaluation approaches using real robots. Lastly, we discuss the limitations and present future research directions. © 2024 IEEE.

关键词： Machine design

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UniSaT: Unified-Objective Belief Model and Planner to Search for and Track Multiple Objects

UniSaT: Unified-Objective Belief Model and Planner to Search...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Santos, Leonardo Moon, Brady Scherer, Sebastian Nguyen, Hoa Van School of Engineering Federal University of Minas Gerais MG Belo Horizonte Brazil Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States Department of Electrical and Computer Engineering Curtin University BentleyWA6102 Australia

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Path planning for autonomous search and tracking of multiple objects is a critical problem in applications such as reconnaissance, surveillance, and data gathering. Due to the inherent competing objectives of searching for new objects while maintaining tracks for found objects, most current approaches rely on multi-objective planning methods, leaving it up to the user to tune parameters to balance between the two objectives, usually based on heuristics or trial and error. In this paper, we introduce UniSaT (Unified Search and Track), a novel unifiedobjective formulation for the search and track problem based on Random Finite Sets (RFS). Our approach models unknown and known objects using a combined generalized labeled multi-Bernoulli (GLMB) filter. For unseen objects, UniSaT leverages both cardinality and spatial prior distributions, allowing it to operate without prior knowledge of the exact number of objects in the search space. The planner maximizes the mutual information of this unified belief model, creating balanced search and tracking behaviors. We demonstrate our work in a simulated environment, presenting both qualitative results and quantitative improvements over a multi-objective method. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Object tracking

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